AI as Shared Business Infrastructure: Firm Growth and the Distribution of Gains

Panle Barwick (), Lu Fang, Jinhan Liu, Huilan Xu and Zhe Yuan

No 36003, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Generative AI improves performance on individual tasks, but whether those gains translate into firm growth, and for which firms, remains an open question. We study a leading Chinese e-commerce platform that in 2024 embedded a generative AI system in the operating software firms use to run their businesses, drawing on centrally developed models and platform-wide data to deliver ready-to-use applications to every firm. Complete records of each firm’s AI use, linked to its monthly operations and sales, let us trace adoption through changes in business capabilities, such as reaching customers and introducing new products, to sales growth. Using a matched difference-in-differences design and focusing on firms with annual sales below RMB 5 million, we find that adoption raises monthly sales by 51.6%, primarily through a 47.0% increase in the number of orders; instrumental-variable estimates confirm these results. Adopters reach more customers, update their product lines faster, and deliver better product and service quality, with the largest proportional gains for the smallest firms. Two mechanisms help explain the results. First, AI lowers the cost of routine work: adopters produce more product content and change listings more often while spending less staff time per order, and the gains are largest for firms that faced the highest costs relative to sales before adoption. Second, AI reduces information frictions: adopters post richer product information, and the gains are largest in markets where demand is hard to predict and matching quality is poor, especially for small firms. At the market level, product categories with greater pre-existing information frictions see more sales by existing firms, more entry, and more new products. AI expands markets rather than merely shifting sales from non-adopters to adopters.

JEL-codes: D22 L25 L81 L86 O33 (search for similar items in EconPapers)
Date: 2026-11
Note: IO
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.nber.org/papers/w36003.pdf (application/pdf)
Access to the full text is generally limited to series subscribers, however if the top level domain of the client browser is in a developing country or transition economy free access is provided. More information about subscriptions and free access is available at http://www.nber.org/wwphelp.html. Free access is also available to older working papers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:36003

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w36003
The price is Paper copy available by mail.

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().